Two-stage support vector machines for protein secondary structure prediction
Neural, Parallel & Scientific Computations - Special issue: Advances in intelligent systems and applications
The application of structured learning in natural language processing
Machine Translation
Mercer’s theorem, feature maps, and smoothing
COLT'06 Proceedings of the 19th annual conference on Learning Theory
PRIB'12 Proceedings of the 7th IAPR international conference on Pattern Recognition in Bioinformatics
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We investigate if interactions of longer range than typically considered in local protein secondary structure prediction methods can be captured in a simple machine learning framework to improve the prediction of β sheets. We use support vector machines and recursive feature elimination to show that the small signals available in long range interactions can indeed be exploited. The improvement is small but statistically significant on the benchmark datasets we used. We also show that feature selection within a long window and over amino acids at specific positions typically selects amino acids that are shown to be more relevant in the initiation and termination of β-sheet formation.